library(rstan)
library(survival)
library(tidyverse)
library(tidybayes)
# data, parameters, model and generated quantities blocks
Stan_exponential_survival_model <- "
data{
int <lower=1> N_uncensored;
int <lower=1> N_censored;
int <lower=0> numCovariates;
matrix[N_censored, numCovariates] X_censored;
matrix[N_uncensored, numCovariates] X_uncensored;
vector <lower=0>[N_censored] times_censored;
vector <lower=0>[N_uncensored] times_uncensored;
}
parameters{
vector[numCovariates] beta; //regression coefficients
real alpha; //intercept
}
model{
beta ~ normal(0,10); //prior on regression coefficients
alpha ~ normal(0,10); //prior on intercept
target += exponential_lpdf(times_uncensored | exp(alpha+X_uncensored * beta)); //log-likelihood part for uncensored times
target += exponential_lccdf(times_censored | exp(alpha+X_censored * beta)); //log-likelihood for censored times
}
generated quantities{
vector[N_uncensored] times_uncensored_sampled; //prediction of death
for(i in 1:N_uncensored) {
times_uncensored_sampled[i] = exponential_rng(exp(alpha+X_uncensored[i,]* beta));
}
}
"
# prepare the data
set.seed(42);
require (tidyverse);
data <- read_csv('../data.csv')
N <- nrow (data);
data$developers_count <- car::recode(data$author_count, "0:20 = 0; 21:max(data$author_count) = 1")
X <- as.matrix(pull(data, developers_count));
is_censored <- pull(data, status)==0;
times <- pull(data, duration);
msk_censored <- is_censored == 1;
N_censored <- sum(msk_censored);
# put data into a list for Stan
Stan_data <- list (N_uncensored = N - N_censored,
N_censored = N_censored,
numCovariates = ncol(X),
X_censored = as.matrix(X[msk_censored,]),
X_uncensored = as.matrix(X[!msk_censored ,]),
times_censored = times[msk_censored],
times_uncensored = times[!msk_censored])
# fit Stan model
require(rstan)
exp_surv_model_fit <- suppressMessages(stan(model_code = Stan_exponential_survival_model, data = Stan_data))
Warning in system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :
error in running command
clang: error: no input files
SAMPLING FOR MODEL 'bf5dbbde6a245330de71a285e3fe7c42' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.000472 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.72 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 0.920697 seconds (Warm-up)
Chain 1: 0.965172 seconds (Sampling)
Chain 1: 1.88587 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'bf5dbbde6a245330de71a285e3fe7c42' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0.000164 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.64 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 2:
Chain 2: Elapsed Time: 0.952962 seconds (Warm-up)
Chain 2: 0.946664 seconds (Sampling)
Chain 2: 1.89963 seconds (Total)
Chain 2:
SAMPLING FOR MODEL 'bf5dbbde6a245330de71a285e3fe7c42' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0.00017 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.7 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 3:
Chain 3: Elapsed Time: 0.939159 seconds (Warm-up)
Chain 3: 0.949951 seconds (Sampling)
Chain 3: 1.88911 seconds (Total)
Chain 3:
SAMPLING FOR MODEL 'bf5dbbde6a245330de71a285e3fe7c42' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 0.000166 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 1.66 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4:
Chain 4:
Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 4:
Chain 4: Elapsed Time: 0.934158 seconds (Warm-up)
Chain 4: 0.872527 seconds (Sampling)
Chain 4: 1.80669 seconds (Total)
Chain 4:
# print model fit
print(get_seed(exp_surv_model_fit))
[1] 1781592037
# print fit summary
fit_summary <- summary(exp_surv_model_fit)
print(fit_summary$summary)
mean se_mean sd 2.5% 25% 50%
beta[1] -1.409013 0.0026280208 0.13244960 -1.677495 -1.499322 -1.407036
alpha -4.738585 0.0008769689 0.04167914 -4.822669 -4.765333 -4.738450
times_uncensored_sampled[1] 114.748918 1.8370043338 114.24553835 3.157978 31.876633 76.836579
times_uncensored_sampled[2] 119.865441 1.8379226178 117.43843959 3.571390 35.665890 83.166967
times_uncensored_sampled[3] 114.511160 1.9103783650 112.92657636 3.537449 33.433981 80.070856
times_uncensored_sampled[4] 113.960292 1.8331149051 116.99752908 2.687777 31.881852 77.989772
times_uncensored_sampled[5] 114.438091 1.8524776854 116.51590842 3.163008 32.870728 77.891142
times_uncensored_sampled[6] 114.628263 1.8511767299 115.03248139 2.791244 35.039902 80.157283
times_uncensored_sampled[7] 114.634797 1.7822040061 111.80496164 3.209696 33.836658 81.646803
times_uncensored_sampled[8] 110.606231 1.6767670944 107.79497976 3.101947 32.820210 78.275663
times_uncensored_sampled[9] 114.836614 1.7926905642 114.89542649 2.808582 33.518333 79.299587
times_uncensored_sampled[10] 113.374099 1.8266588379 115.56884273 2.886520 32.800476 77.755367
times_uncensored_sampled[11] 113.650335 1.7888860391 112.78069369 3.046404 32.286506 80.616718
times_uncensored_sampled[12] 113.605358 1.7908974361 116.28187003 3.247074 32.807655 78.403829
times_uncensored_sampled[13] 116.885793 1.7902936016 116.50220590 3.193254 34.317733 80.569119
times_uncensored_sampled[14] 112.367649 1.7155222095 109.24625490 2.801732 32.487841 78.494594
times_uncensored_sampled[15] 112.637915 1.9671525712 116.88261677 2.904535 30.670703 76.125703
times_uncensored_sampled[16] 111.991202 1.8093068784 112.77116368 2.656018 32.678008 78.227254
times_uncensored_sampled[17] 115.036033 1.7812775852 112.39681743 2.883118 32.394891 81.988009
times_uncensored_sampled[18] 113.199662 1.8067192031 111.46896149 2.457262 32.658051 80.556648
times_uncensored_sampled[19] 111.807403 1.7351302103 111.10011613 2.489032 30.948792 77.335706
times_uncensored_sampled[20] 119.066032 1.8371080315 114.26181989 3.160901 36.503804 84.326699
times_uncensored_sampled[21] 115.696864 1.9758857408 119.01287876 2.702289 33.067590 77.401768
times_uncensored_sampled[22] 113.072576 1.7118997946 112.37001665 3.211566 32.877209 76.891102
times_uncensored_sampled[23] 112.545454 1.7396843989 109.46249129 2.611059 31.865179 78.433535
times_uncensored_sampled[24] 113.519407 1.8045931228 112.94733037 3.125745 32.495333 78.559896
times_uncensored_sampled[25] 116.017984 1.8373704882 115.70073992 2.760987 34.150191 80.633314
times_uncensored_sampled[26] 111.345324 1.8150472209 115.15333194 3.239513 32.234860 74.725288
times_uncensored_sampled[27] 115.067006 1.8534282649 116.71002304 3.008279 33.029258 80.119160
times_uncensored_sampled[28] 112.837641 1.8157135006 115.08937341 3.206752 32.336684 77.579044
times_uncensored_sampled[29] 114.328866 1.8161426626 111.29027111 2.975488 31.859426 80.719429
times_uncensored_sampled[30] 116.210934 1.8510548563 117.15572273 3.336195 33.383577 80.922439
times_uncensored_sampled[31] 117.164604 1.8987946608 118.63332578 2.847351 33.346237 81.038764
times_uncensored_sampled[32] 112.180731 1.8291462698 111.72743728 2.399076 32.417590 76.258349
times_uncensored_sampled[33] 113.502459 1.7412820191 112.78171457 2.789170 31.496763 77.094567
times_uncensored_sampled[34] 114.513119 1.8190764347 113.52550330 3.081498 34.088632 79.732524
times_uncensored_sampled[35] 116.141382 1.8729361156 115.58879329 2.839435 33.399207 80.156472
times_uncensored_sampled[36] 114.248707 1.7817820953 112.43379696 3.093695 34.370537 80.577117
times_uncensored_sampled[37] 114.839766 1.7746807041 114.26923422 3.184977 33.874242 80.362571
times_uncensored_sampled[38] 115.303432 1.8742301174 116.43443953 3.043613 32.357545 79.398164
times_uncensored_sampled[39] 113.611277 1.8354823534 114.79333078 3.209168 32.626455 79.704459
times_uncensored_sampled[40] 116.273554 1.9222867626 116.04661457 2.596509 32.898826 81.283702
times_uncensored_sampled[41] 117.081689 1.7991216989 112.21660644 3.127863 34.317099 80.619777
times_uncensored_sampled[42] 114.885094 1.9123095363 116.33872129 2.783404 33.055964 79.394374
times_uncensored_sampled[43] 112.496903 1.8379038459 114.85942044 3.338855 32.566303 77.496419
times_uncensored_sampled[44] 116.330856 1.8577357792 118.21266116 3.273292 34.345987 81.061137
times_uncensored_sampled[45] 111.921883 1.7198429977 110.62277201 2.913107 32.704207 78.881405
times_uncensored_sampled[46] 112.951387 1.7684061510 111.29148458 3.374646 32.974034 76.293381
times_uncensored_sampled[47] 116.616029 1.8688805617 121.73772937 3.232953 32.673380 80.422343
times_uncensored_sampled[48] 114.349361 1.7704416009 111.30909589 2.955325 33.099032 81.390648
times_uncensored_sampled[49] 115.694868 2.0118086481 115.70698248 2.409178 31.581084 79.111714
times_uncensored_sampled[50] 113.620053 1.8529501133 112.88083323 2.870059 33.275285 79.281213
times_uncensored_sampled[51] 113.830507 1.9358051845 112.16312600 2.901751 33.865021 80.014333
times_uncensored_sampled[52] 117.499550 1.9307093110 117.20545205 3.045203 33.478916 82.485439
times_uncensored_sampled[53] 112.841261 1.7730325398 110.65521692 3.152006 33.461911 79.205780
times_uncensored_sampled[54] 113.770263 1.7927570773 112.76609155 2.958088 32.097371 81.286467
times_uncensored_sampled[55] 115.087694 1.9499257245 118.45164434 2.871020 32.748280 79.074054
times_uncensored_sampled[56] 115.256435 1.9183405284 119.32777353 2.645240 33.439652 79.620046
times_uncensored_sampled[57] 110.818768 1.7229284971 112.63390725 2.408834 31.008527 76.493712
times_uncensored_sampled[58] 116.751737 1.8823064685 114.57534101 2.898587 34.081322 81.536092
times_uncensored_sampled[59] 114.838068 1.7914457486 113.87210869 2.721862 33.514224 81.228918
times_uncensored_sampled[60] 110.787377 1.7616616364 110.82112086 2.525288 32.042694 75.887113
times_uncensored_sampled[61] 112.846216 1.7268667727 111.99672049 2.860086 33.148614 80.330559
times_uncensored_sampled[62] 116.196793 1.8465551029 114.21964823 2.851707 34.390556 82.242353
times_uncensored_sampled[63] 114.114096 1.9166559807 114.04289796 2.759633 32.665407 80.243188
times_uncensored_sampled[64] 113.550064 1.8477042650 114.44566909 3.082074 32.732179 77.931989
times_uncensored_sampled[65] 114.246548 1.7962678453 114.96550863 3.188175 33.119244 79.080655
times_uncensored_sampled[66] 115.290633 1.8533791532 116.23156945 2.599775 33.664838 80.311284
times_uncensored_sampled[67] 115.504903 1.9369595901 118.26002266 3.430321 33.294991 79.524425
times_uncensored_sampled[68] 111.489103 1.8784207601 112.71648672 2.817025 31.867836 77.741179
times_uncensored_sampled[69] 113.191050 1.8159800226 111.98480294 3.225945 31.964963 80.969177
times_uncensored_sampled[70] 115.554476 1.7956420413 112.67921679 3.076370 33.047239 81.694165
times_uncensored_sampled[71] 112.983805 1.8068753432 112.77091926 3.338408 32.676289 79.338673
times_uncensored_sampled[72] 115.506112 1.8769145405 117.18512252 2.252208 30.376360 78.980180
times_uncensored_sampled[73] 115.398797 1.9796057572 116.23832516 3.005857 33.468532 80.346736
times_uncensored_sampled[74] 109.918868 1.7051642733 107.50792456 2.949902 32.510887 76.199371
times_uncensored_sampled[75] 114.183092 1.7973451713 115.95461849 2.613565 30.972511 77.100017
times_uncensored_sampled[76] 114.062714 1.7543792564 111.36182178 3.054476 34.217594 81.975172
times_uncensored_sampled[77] 111.806553 1.7664186958 112.57146748 2.546246 31.098497 77.600707
times_uncensored_sampled[78] 112.849447 1.8645268688 113.73120645 2.737883 32.304376 77.442969
times_uncensored_sampled[79] 112.741887 1.7946539348 114.15292301 3.013043 31.388715 77.819219
times_uncensored_sampled[80] 114.972172 1.8574849528 117.18807319 2.730411 34.117928 77.868103
times_uncensored_sampled[81] 116.422871 1.8704127445 119.82535210 2.662810 32.629442 79.603660
times_uncensored_sampled[82] 116.236444 1.9198737208 115.82155172 3.116008 33.204788 81.434049
times_uncensored_sampled[83] 115.682078 1.9067892168 116.45700354 3.229578 31.772353 80.158096
times_uncensored_sampled[84] 112.305221 1.8168935255 115.42560133 2.958418 31.308990 77.253777
times_uncensored_sampled[85] 118.359469 1.8443093222 118.85027339 3.353921 34.622761 81.080658
times_uncensored_sampled[86] 113.225737 1.7950294700 113.76819578 2.982522 32.676024 77.795693
times_uncensored_sampled[87] 113.690106 1.7230729990 112.24052585 3.311051 32.623213 79.694352
times_uncensored_sampled[88] 114.606652 1.8802951255 117.18356931 2.677411 31.609498 79.107677
times_uncensored_sampled[89] 119.456848 1.8897919111 120.86510915 3.056804 33.842142 82.562988
times_uncensored_sampled[90] 116.988271 1.7762189696 113.77525447 3.037813 34.970472 81.579601
times_uncensored_sampled[91] 115.436560 1.8468780317 113.14308602 3.079217 32.988394 80.737918
times_uncensored_sampled[92] 114.174087 1.9226326089 117.09802221 2.240360 33.861011 77.040066
times_uncensored_sampled[93] 113.711126 1.7895902514 111.64884359 3.132940 32.562543 79.701842
times_uncensored_sampled[94] 115.020440 1.8849813063 115.97666086 3.392841 32.729919 80.576021
times_uncensored_sampled[95] 117.112566 1.8661266468 113.74886001 2.771792 33.810769 82.337642
times_uncensored_sampled[96] 113.786063 1.7479738540 111.63475308 3.534576 33.779504 80.012899
times_uncensored_sampled[97] 113.355202 1.8209458645 113.45630525 2.287952 33.033474 79.341863
times_uncensored_sampled[98] 114.531024 1.7806866689 115.70054445 3.174515 32.634186 79.026891
75% 97.5% n_eff Rhat
beta[1] -1.317992 -1.156991 2540.059 1.0009225
alpha -4.711266 -4.656309 2258.755 1.0003625
times_uncensored_sampled[1] 161.955413 422.799805 3867.748 1.0002362
times_uncensored_sampled[2] 165.638005 440.759189 4082.875 0.9995951
times_uncensored_sampled[3] 159.952686 410.668972 3494.247 1.0010001
times_uncensored_sampled[4] 156.707210 427.803829 4073.559 0.9996277
times_uncensored_sampled[5] 157.047347 423.260615 3956.075 1.0000341
times_uncensored_sampled[6] 154.920048 428.631268 3861.404 0.9994373
times_uncensored_sampled[7] 160.090786 407.742986 3935.567 1.0003424
times_uncensored_sampled[8] 154.598463 401.535556 4132.868 0.9995167
times_uncensored_sampled[9] 161.194317 426.852117 4107.663 0.9993255
times_uncensored_sampled[10] 155.552764 419.340695 4002.825 1.0000438
times_uncensored_sampled[11] 158.273618 409.008339 3974.698 1.0001663
times_uncensored_sampled[12] 155.983423 420.910797 4215.825 0.9995505
times_uncensored_sampled[13] 161.772404 432.120385 4234.672 1.0003957
times_uncensored_sampled[14] 160.213421 399.431016 4055.276 0.9995332
times_uncensored_sampled[15] 151.760281 427.530819 3530.399 0.9999401
times_uncensored_sampled[16] 157.098266 408.721155 3884.827 1.0015108
times_uncensored_sampled[17] 161.602866 403.815203 3981.483 1.0000615
times_uncensored_sampled[18] 160.186091 397.066954 3806.507 1.0005329
times_uncensored_sampled[19] 158.019518 404.740151 4099.821 0.9999738
times_uncensored_sampled[20] 164.649094 425.521214 3868.413 1.0004050
times_uncensored_sampled[21] 159.540241 425.706796 3627.975 0.9998521
times_uncensored_sampled[22] 157.623206 422.771691 4308.680 0.9997726
times_uncensored_sampled[23] 161.231037 400.322760 3959.038 0.9993953
times_uncensored_sampled[24] 156.819426 414.522442 3917.359 1.0000359
times_uncensored_sampled[25] 159.894413 427.840280 3965.325 1.0007620
times_uncensored_sampled[26] 147.203480 430.097548 4025.105 1.0011261
times_uncensored_sampled[27] 157.813409 426.252452 3965.197 0.9994729
times_uncensored_sampled[28] 151.135519 416.785122 4017.684 0.9994477
times_uncensored_sampled[29] 162.580430 404.942695 3755.039 1.0001733
times_uncensored_sampled[30] 160.836048 420.937369 4005.792 0.9994989
times_uncensored_sampled[31] 161.353671 427.904564 3903.529 0.9999427
times_uncensored_sampled[32] 155.697074 427.122387 3730.979 1.0004681
times_uncensored_sampled[33] 162.244959 415.088666 4195.069 0.9995157
times_uncensored_sampled[34] 158.332089 420.749443 3894.798 1.0002163
times_uncensored_sampled[35] 161.042165 433.703813 3808.777 1.0000926
times_uncensored_sampled[36] 159.132189 414.163891 3981.847 1.0006859
times_uncensored_sampled[37] 160.181730 422.070608 4145.894 0.9997565
times_uncensored_sampled[38] 162.517505 427.424687 3859.376 1.0007154
times_uncensored_sampled[39] 155.596905 427.859781 3911.406 1.0001274
times_uncensored_sampled[40] 161.014202 420.680264 3644.421 0.9995628
times_uncensored_sampled[41] 166.615353 416.072343 3890.390 0.9998858
times_uncensored_sampled[42] 158.689581 435.857625 3701.112 0.9993080
times_uncensored_sampled[43] 153.127521 422.611505 3905.599 1.0018958
times_uncensored_sampled[44] 159.144764 434.028697 4049.115 0.9992360
times_uncensored_sampled[45] 153.324369 408.067409 4137.247 0.9992185
times_uncensored_sampled[46] 158.013746 412.709700 3960.590 1.0017098
times_uncensored_sampled[47] 158.213250 437.005699 4243.139 1.0001253
times_uncensored_sampled[48] 159.737747 400.506624 3952.739 1.0004808
times_uncensored_sampled[49] 161.683176 413.077576 3307.850 1.0004926
times_uncensored_sampled[50] 159.468350 404.803235 3711.188 0.9996495
times_uncensored_sampled[51] 156.954959 419.554978 3357.198 1.0016555
times_uncensored_sampled[52] 161.251528 447.407075 3685.207 1.0000838
times_uncensored_sampled[53] 156.931686 408.424894 3895.026 0.9991541
times_uncensored_sampled[54] 158.319615 421.953452 3956.527 0.9998863
times_uncensored_sampled[55] 156.508808 430.845189 3690.167 0.9998333
times_uncensored_sampled[56] 156.713885 444.425173 3869.293 0.9993080
times_uncensored_sampled[57] 153.413695 414.756300 4273.698 1.0013239
times_uncensored_sampled[58] 162.149492 417.951081 3705.115 1.0009627
times_uncensored_sampled[59] 159.244823 425.299710 4040.428 0.9999408
times_uncensored_sampled[60] 153.998114 421.886335 3957.310 0.9996097
times_uncensored_sampled[61] 156.150197 418.385312 4206.229 1.0008811
times_uncensored_sampled[62] 164.320699 414.766703 3826.107 1.0002396
times_uncensored_sampled[63] 157.916896 416.251239 3540.366 1.0003962
times_uncensored_sampled[64] 155.166883 421.199468 3836.488 1.0004331
times_uncensored_sampled[65] 157.297271 421.399599 4096.311 1.0014714
times_uncensored_sampled[66] 157.337324 424.943574 3932.961 1.0002028
times_uncensored_sampled[67] 160.199226 427.461442 3727.647 0.9995690
times_uncensored_sampled[68] 154.625786 411.755558 3600.718 0.9994482
times_uncensored_sampled[69] 156.422322 405.619438 3802.735 1.0004203
times_uncensored_sampled[70] 161.708415 425.181876 3937.750 1.0001543
times_uncensored_sampled[71] 157.118771 408.063583 3895.273 0.9992953
times_uncensored_sampled[72] 163.773339 420.192794 3898.127 0.9997815
times_uncensored_sampled[73] 159.042114 426.907690 3447.794 1.0010191
times_uncensored_sampled[74] 153.189474 398.692659 3975.104 1.0000231
times_uncensored_sampled[75] 160.961363 419.935461 4162.106 0.9995250
times_uncensored_sampled[76] 159.291137 415.205725 4029.264 1.0003625
times_uncensored_sampled[77] 155.639810 413.788967 4061.340 1.0012144
times_uncensored_sampled[78] 158.527177 423.461965 3720.677 0.9999695
times_uncensored_sampled[79] 156.045800 420.397745 4045.877 1.0003798
times_uncensored_sampled[80] 155.497658 432.719118 3980.304 0.9996282
times_uncensored_sampled[81] 161.746559 438.321115 4104.143 0.9993148
times_uncensored_sampled[82] 162.619374 419.944353 3639.430 1.0003399
times_uncensored_sampled[83] 157.947923 431.269202 3730.146 1.0000390
times_uncensored_sampled[84] 151.703691 413.567411 4035.946 0.9999081
times_uncensored_sampled[85] 163.086731 442.599622 4152.722 0.9996571
times_uncensored_sampled[86] 158.099839 424.710332 4016.970 1.0000976
times_uncensored_sampled[87] 158.670119 415.034147 4243.186 1.0006678
times_uncensored_sampled[88] 158.371560 430.346827 3884.020 0.9995753
times_uncensored_sampled[89] 163.038953 457.431318 4090.477 0.9997118
times_uncensored_sampled[90] 162.170515 417.404612 4103.010 1.0004230
times_uncensored_sampled[91] 164.202215 416.543906 3753.009 1.0000339
times_uncensored_sampled[92] 158.776604 435.544721 3709.424 0.9997599
times_uncensored_sampled[93] 159.722097 417.895148 3892.255 1.0007411
times_uncensored_sampled[94] 158.600093 424.783157 3785.534 0.9995541
times_uncensored_sampled[95] 165.923726 423.822685 3715.454 0.9996369
times_uncensored_sampled[96] 157.158215 416.471579 4078.768 1.0005156
times_uncensored_sampled[97] 155.108564 406.811363 3882.069 1.0003268
times_uncensored_sampled[98] 159.027113 422.238205 4221.782 0.9993589
[ reached getOption("max.print") -- omitted 547 rows ]
exp_surv_model_draws <- tidybayes::tidy_draws(exp_surv_model_fit)
exp_surv_model_draws